(295b) Eagle Ford Case Study and Optimization Using Implemented Fracture Production Model

This paper describes an automated workflow to analyze historical production data and predict future production and economical value for fracture-stimulated condensate reservoirs. A numerical model is developed and implemented as part of the workflow, and it was validated using the historical production data of an Eagle Ford well. Outputs of this workflow can provide predicted production rate based on well completion, fracture geometry, and reservoir and wellbore flows. it may be used for history matching, completion optimization, and problem diagnosis.

A fracture production and optimization model (FPOM) has been realized in commercial software and used for reservoir productivity and fracture parameter sensitivity analysis. Using the FPOM, an automated history matching and optimization workflow was developed and validated on an Eagle Ford well. The stage optimization was conducted to minimize the discrepancy between the predicted and measured production decline curves (history matching). Then future prediction and economics sensitivity analysis was carried out based on the early stage production.

Because this workflow is dynamically combined with the FPOM, variations in the reservoir properties, fracture parameters, and operational conditions are automatically analyzed and determined. Predictions of future production and economics are provided to direct operation. Upper and lower limits of the future outcome are provided by a statistical prediction based on the best knowledge from the early stage transient analysis of the reservoir depletion and production. Best fit methods and type curves are used together with the matched data to provide insight for production surveillance.

By adopting simplified fracture geometry without neglecting physical effects, the developed numerical model can be used to perform fast production decline analysis with a detailed account for condensate properties, including the phase transitions. The effectiveness of the model makes it possible to run very quickly, which enables efficient fracture optimization and automated history matching.

This workflow is efficient in the form of application for wellbore solvers with condensate, reducing the need for coupling with 3D reservoir solvers. The high efficiency is key to parameter sensitivity analysis, which enables automatic history matching. The parameters obtained from this thorough analysis will, in turn, benefit the prediction of future production and surveillance.